Summary of Causally Inspired Regularization Enables Domain General Representations, by Olawale Salaudeen and Sanmi Koyejo
Causally Inspired Regularization Enables Domain General Representations
by Olawale Salaudeen, Sanmi Koyejo
First submitted to arxiv on: 25 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores how to identify domain-general feature representations from a given causal graph representing the data-generating process across different domains. By enforcing sufficient graph-implied conditional independencies, it is possible to identify these non-spurious features. The authors categorize existing methods into two groups: those that naturally provide domain-general representations and those that do not. For the latter case, they propose a novel framework with regularizations that can identify domain-general feature representations without prior knowledge of spurious features. Experimental results demonstrate the effectiveness of this approach on both synthetic and real-world data, outperforming other state-of-the-art methods in average and worst-domain transfer accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to get useful information from a graph that shows how different things are connected. It wants to find features that work across many different situations, not just one special case. The authors look at what others have done and divide it into two groups: some methods naturally give good results, while others don’t. For those that don’t, they suggest a new way of doing things that works better without needing extra information about what’s important or what’s not. They test this new approach with fake and real data and show that it does better than other ways of doing things. |
